Exercise 1

• metrics_5year.xlsx
• tickers.xlsx
• Download from course website and upload to Julius.
• Maybe better to upload in two messages rather than one.

Explore data

• How many rows in the tickers dataset? What are the columns?
• What are the columns in metrics_5year?
• Group metrics_5year by date and count the number of tickers at each date.

Merge

• Do an inner merge of tickers and metrics_5year on ticker.
  
• Group by date and count the number of tickers at each date.
• Save the merged dataset as merged.xlsx.

Explore visually

We want to see how the metrics have changed over time and how they vary across sectors. Suggestions:

• Filter to the date 2023-12-29, group by sector and generate barplots of aggregates (total or median) - e.g., total marketcap or median pb.
• Group by (sector, date) and generate 3d barplots of aggregates.
• Group by (sector, date) and generate heatmaps of aggregates.

Exercise 2

• Ask Julius to use yfinance to get closing prices for CVX during April 2020 (if error, ask to pip install yfinance==0.1.70).
• Ask Julius to use pandas datareader to get crude oil prices from FRED during April 2020.
• Ask Julius to plot the CVX prices and crude oil prices in the same figure with the crude price labels on a second y axis.

Exercise 3

• Ask Julius to get the histories since 1990 of the 3-month, 1-year, 5-year, and 10-year Treasury yields from FRED.
• Ask Julius to downsample to first day of the month.
• Ask Julius to use plotly to create an animation with the yield on the y axis and the time to maturity on the x axis and using the date as the animation frame.
• Ask Julius to save it as html.